Quantitative Study of the Resolving Power of Arrays of Carbon Black−Polymer Composites in Various Vapor-Sensing Tasks

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Abstract
A statistical metric, based on the magnitude and standard deviations along linear projections of clustered array response data, was utilized to facilitate an evaluation of the performance of detector arrays in various vapor classification tasks. This approach allowed quantification of the ability of a 14-element array of carbon black−insulating polymer composite chemiresistors to distinguish between members of a set of 19 solvent vapors, some of which vary widely in chemical properties (e.g., methanol and benzene) and others of which are very similar (e.g., n-pentane and n-heptane). The data also facilitated evaluation of questions such as the optimal number of detectors required for a specific task, whether improved performance is obtained by increasing the number of detectors in a detector array, and how to assess statistically the diversity of a collection of detectors in order to understand more fully which properties are underrepresented in a particular set of array elements. In addition, the resolving power of arrays of carbon black−polymer composites was compared to the resolving power of specific collections of bulk conducting organic polymer or tin oxide detector arrays in a common set of vapor classification tasks.