Information System and User Model for Predictive Analysis of Nano-Material Attributes

This Phase I SBIR program will develop an information system and design a nano-Toolkit with data-mining and predictive models for accelerated risk assessment of engineered nanomaterial attributes. It was recently shown that nanomaterials used in warfighter technologies may be responsible for significant human health and environmental risks. For example, researchers at the Army’s Engineer Research and Development Center have recently found that nanoscale metal-oxide particles are more toxic than micron-sized particles. The further progress in rapid and reliable prediction of nanomaterial toxicity is limited by (1) limited integration or use of data from published literature for predicting attributes of new materials, and (2) a lack of models to predict unavailable attributes. Those deficiencies will be addressed in the proposed work. During Phase I, utilizing CFDRC complementary expertise, we propose to develop a wiki style information system linked to a nano-Toolkit integrating models with different level of details from Quantum Mechanic based ReaxFF model to quantitative structure-activity relationship (QSAR) algorithms. In Phase II, the information system and nano-Toolkit will be further developed, and demonstrated to predict environmental attributes (degradation potential, fate in soils, toxicity, and bioaccumulation potential) of engineered nanomaterials of direct interest to DoD.