TY - CONF A1 - Heinrich, Thomas A1 - Schneider, Markus A1 - Schäpe, Kaija A1 - Unger, Wolfgang A1 - Stockmann, Jörg M. T1 - Classification of engineered Titania nanomaterials via surface analysis using principal component analysis (PCA) assisted Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) N2 - Due to the growing number of engineered nanomaterials (NM) the need for a reliable risk assessment for these materials is today bigger than ever before. Especially the nanomaterial’s surface or shell directly interacts with its environment and therefore is a crucial factor for NM’ toxicity or functionality. Especially, titania is one of the NM with the greatest technological importance. It is used for a large number of applications and can be found in food, cosmetics, glasses, mirrors, paints to mention only a few. In 2012, experts estimate[d] the annual European nano-titania production or utilization at an amount of more than 10,000 t. Great progress has been achieved in the area of NM investigation and characterization during the past decade. A variety of publications provide information about technological innovation as well as hazard potential, which means the potential risk on human health and ecosystems. However, enhanced data harmonization and well-defined standards for nanomaterial analysis, could significantly improve the reliability of such studies which often suffers from varying methods, parameters and sample preparations. To develop a suitable approach for the NM’s risk assessment, the ACEnano project aims at establishing a toolbox of verified methods. The size of this well-structured European project allows to handle even those big challenges like data harmonization and standardization. Due to its powerful combination of superior surface sensitivity and lateral resolution down to the Nano regime, ToF-SIMS could become one of these toolbox methods. Supported by multivariate data analysis such as principal component analysis (PCA), the method can be used for sub-classification of nanomaterial families using slight differences in surface chemistry. Here, we show a PCA supported classification of titania nanoparticles from various sources (NIST, JRC, BAM) with ToF-SIMS. Parameters like size, shell, pre-preparation and crystal system cause variance in the data and allow us to distinguish the species from each other. Moreover, this variance in the data also occurs and can be used for investigation when we compare our measurements of particle ensembles with those of grown titania films. The carefully selected and refined peaks allow a reasonable particle categorization and further a reliable allocation of blank feeds, which introduces a promising approach for NM characterization in the context of NM risk assessment. T2 - SIMS-Europe CY - Münster, Germany DA - 16.09.2018 KW - Nanoparticles KW - ToF-SIMS KW - XPS KW - PCA KW - Titania PY - 2018 AN - OPUS4-46250 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -