ABSTRACT
New nanomaterials make comprehensive testing more difficult and costly. Categorization technology is being applied to nanomaterials, such as expansion of quantitative structure activity relationship technology (QSAR), to reduce testing burdens. Success is dependent critically on accurate categorization. To date nanomaterials categorization schemes have used chemical composition, shape, size, size distribution, surface coatings, and other properties. No categorization procedure, however, has yet been successful in predicting important properties, especially those related to risk to health or environment. In this paper, we define a set of goals for nanomaterials categorization and discuss them in detail. One problem is the lack of precision in describing nanomaterials well enough to correlate specific features with important properties to determine cause and effect. We present a systematic approach to describing nanomaterials that supports categorization and identifies the information needed. We clearly differentiate between an individual piece of nanomaterial (nano-object) and collections of nano-objects and highlight the importance of differentiating among different types of nanomaterials, including changes during their life cycle. We discuss how this description system supports more precise nanomaterials characterization by enabling more precise correlation of properties to specific features, an important component of successful categorization. We conclude with thoughts about the importance of using natural language in description systems and categorization.
