Extraction of spatiotemporal response information from sorption-based cross-reactive sensor arrays for the identification and quantification of analyte mixtures

Abstract

Linear sensor arrays made from small molecule/carbon black composite chemiresistors placed in a low headspace volume chamber, with vapor delivered at low flow rates, allowed for the extraction of chemical information that significantly increased the ability of the sensor arrays to identify vapor mixture components and to quantify their concentrations. Each sensor sorbed vapors from the gas stream to various degrees. Similar to gas chromatography, species having high vapor pressures were separated from species having low vapor pressures. Instead of producing typical sensor responses representative of thermodynamic equilibrium between each sensor and an unchanging vapor phase, sensor responses varied depending on the position of the sensor in the chamber and the time from the beginning of the analyte exposure. This spatiotemporal (ST) array response provided information that was a function of time as well as of the position of the sensor in the chamber. The responses to pure analytes and to multi-component analyte mixtures comprised of hexane, decane, ethyl acetate, chlorobenzene, ethanol, and/or butanol, were recorded along each of the sensor arrays. Use of a non-negative least squares (NNLS) method for analysis of the ST data enabled the correct identification and quantification of the composition of 2-, 3-, 4- and 5-component mixtures from arrays using only 4 chemically different sorbent films and sensor training on pure vapors only. In contrast, when traditional time- and position-independent sensor response information was used, significant errors in mixture identification were observed. The ability to correctly identify and quantify constituent components of vapor mixtures through the use of such ST information significantly expands the capabilities of such broadly cross-reactive arrays of sensors.